CN103886074A - Commodity recommendation system based on social media - Google Patents
Commodity recommendation system based on social media Download PDFInfo
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Abstract
The invention discloses a commodity recommendation system based on social media. The commodity recommendation system comprises a social medium data capture module, an electronic commerce data capture module, an information extraction fusion and comparison module and a commodity recommendation module, wherein the social medium data capture module is used for capturing social medium data; the electronic commerce data capture module is used for capturing electronic commerce website data; the information extraction fusion and comparison module is used for analyzing and processing the captured data, carrying out establishing and mapping of commodity trees on commodities and carrying out modeling analysis on social medium users; the commodity recommendation module is used for conducting commodity inquiry on users to be recommended and recommending the commodities according to relevancy information, interest information and friend circle information. The system integrates the social media with electronic commerce for marketing and achieves commodity recommendation through the social media and electronic commerce under the circumstance of no client subjectivity, and commodity purchasing interests of users can be fully excited.
Description
Technical field
The present invention relates to network commodity application, specifically a kind of commercial product recommending system based on social media.
Background technology
Along with the development of Web2.0 and universal, social networks is more and more by numerous Internet users are accepted.User utilizes internet to carry out doings, promotes oneself, shows emotion, or makes friends.User's interest and preference all shown in all activities on social networks.In recent years, cash for the flow of social networks the Forward researches that becomes gradually Internet advertising and commending system.Success is to user's Recommendations and be effectively converted into click and then purchase, can form the similar Internet industry scale of display advertisement of similar search advertisement and door network.Therefore are, very actual problems to the user's Recommendations on social media.
But Recommendations are problems of a relative difficult on social networks.For example, the word that user delivers and image content perhaps do not contact directly with commodity that (user likes sending out the picture in famous mountains and great rivers, and the picture of tourism), if recommend some commodity (as outdoor products) at least can not make its dislike to him, even likely trigger and click.Therefore, how to find user's interest, and can excavate and commodity in use between relation be the problem that this patent will solve.。
The prior art generally similarity of the Collaborative Recommendation based on friend or content-based understanding is recommended.
Collaborative Recommendation based on friend need to be known friend's hobby conventionally, and for example A has 20 friends, wherein has 10 people to like film, can predict that A also probably likes film.The benefit of doing is like this information that takes full advantage of social networks, and the recommendation of hobby based on friend is more accurately.But, on a lot of social networks, the information of the commodity that friend does not buy or pays close attention to.Therefore, the recommendation based on friend is easy to run into the problem of " cold start-up ", has no idea to start prediction.Recommendation based on friend can become very useful signal, the commercial product recommending on and if only if social networks and pay close attention to while forming certain scale just more effective.
The recommendation of content-based understanding refers to that the article content that social networks user is delivered understands, and then estimates its preference.In addition, the label information on social networks can be used for describing user's preference.These preferences can be used for mating commodity.For example, certain user has label " open air ", and outdoor products just becomes good alternative Recommendations so.Although this method can be used for the preference of estimating user, can not well be described user in fine granularity.For example, while recommending outdoor products, be row mountain on earth, or diving; Or be very expensive anorak on earth, still relatively cheap slide plate.These problems are used that label is all more difficult to be judged.
For example, patent publication No. CN102479366 discloses a kind of Method of Commodity Recommendation and system, determine that by obtaining user's behavioral data user's interest commodity are tired, promote shopping impression, still, this application must rely on the behavioral data with user, be click, the search behavior data of user in website, commercial product recommending after the subjective behavior of client, lags behind client's behavior, and user's commodity purchasing interest is not fully excavated.
For example, patent publication No. CN102592223 discloses a kind of Method of Commodity Recommendation and commercial product recommending system, according to user browse record or user property obtain sample training data, commercial product recommending still lags behind client's behavior.
At present, also there is no a kind of fully commending system based on social media information in prior art, user's commodity purchasing does not fully excavate.
Summary of the invention
For solving prior art problem, the invention provides one and utilize various information on social media (content, network etc.), and commodity are effectively understood, form the commercial product recommending system of a system.
Technical scheme of the present invention is: a kind of commercial product recommending system based on social media, comprises social media data handling module, electronic commerce data handling module, information extraction, fusion, comparison module and commercial product recommending module;
Social media data handling module is carried out data crawl for social media, carries out multimachine capture data by parallel algorithms;
Electronic commerce data handling module is carried out data crawl for e-commerce website, carries out multimachine capture data by parallel algorithms, carries out comprehensive data interaction by DeepWeb query expansion;
Information extraction, fusion, comparison module, for to capturing the data analysis, the processing that obtain, carry out foundation and the mapping of commodity tree to commodity, social media user is carried out to modeling analysis.
Commercial product recommending module, for user to be recommended is carried out to merchandise query, sorts to the commodity that obtain, and recommends according to the degree of correlation, interest, circle of friends information;
Social media data handling module, electronic commerce data handling module, commercial product recommending module is all connected with information extraction, fusion, comparison module.
Social media data handling module is obtained the data on social media by web crawlers, obtain after data, distribute to many computing machines by task of using hadoop that many URL are captured, make the scheduling processing method of the load balancing of every computing machine give the distributed system that multi-section server forms, by HTML parser, webpage is analyzed, text analyzing, link analysis and web page quality control, duplicate removal, obtain corresponding web page contents, web page contents result is divided into structured message (friend, the link informations such as group) and unstructured information (text, image etc.), store into respectively in structured message database and the Unstructured Information Database.By distributed system, can process the very information of high-throughput.
Structuring and non-structured classification can be by judging whether this content can store (as SQL) in structured database into and judge.Conventionally text and image are unstructured datas, content wherein cannot be carried out to cutting and classification.As one section of news, although know that there are the information such as name, place name, exabyte, time the inside, if do not processed, cannot import to these information in SQL automatically.
Electronic commerce data handling module produces a series of regular expression query statements by machine learning Query Builder for e-commerce website e-commerce website is inquired about, and all information scratchings are got off, carry out the data interaction of web page contents by DeepWeb query expansion, page analyzer.Learn the rule searching of different web sites by machine learning algorithm, use all inquiries with large probability of method traversal of keyword replacement, by knowledge base intelligent inquire, item property extracts, true value is found, generate entity attribute relational database,, also can be undertaken by historical data training classifier meanwhile.
Information extraction, fusion, comparison module comprise user modeling module, commodity MBM, commodity and user's mapping, modeling module, and user modeling module, commodity MBM are connected with user's mapping, modeling module with commodity.
User modeling module routine comprises:
1-1) label is propagated: for social networks user, carry out random walk by the figure of the label on social networks (label on social networks can be the label of user oneself mark) composition and obtain the probability that label is propagated, thus the label of extending user;
1-2) content is differentiated: the content that user is delivered is analyzed, and uses topic model, entity to extract and obtains possible label; Meanwhile, by training machine learning classification device, the user of existing label is learnt, thereby the user who there is no label is carried out to label judgement;
1-3) other information of user are differentiated: the content of delivering for user is understood and its circle of friends is analyzed, and then estimates user's age, job specification, work place, income information, thereby can better understand user's demand; When age, job specification, work place and income information to user is estimated, user is extracted to keyword and good friend's attributive character, use machine learning method, existing markup information is learnt to obtain sorter, unknown sample is classified.
Be first each user to be estimated to an age to the analysis of circle of friends, this age can be that he fills in the age on social networks, can be also that the content that we deliver him by model of initialization returns the obtained age.And then on social networks, be similar to the process that label is propagated, and this user's circle of friends is analyzed, obtain friend's age bracket statistics, from this user's of row amendment age estimation.
Commodity MBM operational process comprises,
2-1) property value is filled, capturing by webpage the item property obtaining may be comprehensive not, need to search for based on internet search engine, from corresponding summary and ad content, obtain possible property value, and the probability of statistics appearance, in internet, search, mate, add up the frequency of occurrences and carry out true value discovery;
2-2) commodity tree classification, obtains taxonomy-tree information at crawl commodity simultaneously and in addition the commodity that there is no classification tree information is classified, and is categorized on some nodes of commodity tree;
2-3) other information acquisitions of commodity, collect other information of commodity, and store in database, analyze by the structure to e-commerce website, are commented on accordingly and give a mark.
Mapping correspondence set up by commodity and user by commodity and user's mapping, modeling module, the content delivered when user is mentioned to dependent merchandise on social media=and e-commerce website on picture, the comment denoising of user to commodity and businessman, set up mapping model, mapping model is the direct feature extraction to data or the feature representation that obtains by the means of machine learning, obtain after mapping model, relatively commodity and user's correlativity.
Commercial product recommending module comprises recommended by client module and commodity end recommending module, recommended by client module is used for to user's Recommendations, commodity end recommending module is used for this commercial product recommending user, recommended by client module, commodity end recommending module all with, information extraction, fusion, comparison module are connected.
Recommended by client module operational process comprises the following steps:
3-1) obtain by the correlativity of user and commodity the commodity list that may recommend;
3-2) user's good friend is carried out to the analysis of the correlativity of user and commodity, and by good friend's commodity list, this user is voted;
3-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and user's portrait comprises age, income and interest; User's portrait refers to the related content of user modeling module;
3-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of social media comprises adds good friend, quotes good friend, personal letter, comment etc.
Commercial product recommending module operational process comprises the following steps:
4-1) obtaining by commodity and user's correlativity may be to the interested user of these commodity;
4-2) user's good friend is carried out to commodity and user's correlation analysis, and by good friend's commodity list, this user is voted;
4-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and user's portrait comprises age, income and interest;
4-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of social media comprises adds good friend, quotes good friend, personal letter, comment etc.
Beneficial effect of the present invention comprises, the present invention is directed to a social media and ecommerce integration system in conjunction with marketing, by social media and ecommerce, there is no to realize commercial product recommending under the subjective row condition of client, user's commodity purchasing interest can fully be excavated.
Further, native system has effectively utilized the feature of social media and ecommerce, all actively releasing and paying UNICOM's business due at present each large social media platform and e-commerce platform, therefore providing that third-party marketing system will automatic or semi-automatic mode be carried out goods marketing effectively, businessman promotes and advanced level user's realization provides more approach for vast advertiser (electric business) provides.
Brief description of the drawings
Fig. 1 is structural representation of the present invention;
Fig. 2 is that social media data handling module process is processed schematic diagram;
Fig. 3 is electronic commerce data handling module processing procedure schematic diagram;
Fig. 4 is information extraction, fusion, comparison module structural representation.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment, technical solution of the present invention is described in further detail, can be implemented so that those skilled in the art can better understand the present invention also, but illustrated embodiment is not as a limitation of the invention.
As shown in Figure 1, a kind of commercial product recommending system based on social media, comprises social media data handling module, electronic commerce data handling module, information extraction, fusion, comparison module and commercial product recommending module;
Social media data handling module is carried out data crawl for social media, carries out multimachine capture data by parallel algorithms;
Electronic commerce data handling module is carried out data crawl for e-commerce website, carries out multimachine capture data by parallel algorithms, carries out comprehensive data interaction by DeepWeb query expansion;
Information extraction, fusion, comparison module, for to capturing the data analysis, the processing that obtain, carry out foundation and the mapping of commodity tree to commodity, social media user is carried out to modeling analysis.
Commercial product recommending module, for user to be recommended is carried out to merchandise query, sorts to the commodity that obtain, and recommends according to the degree of correlation, interest, circle of friends information;
Social media data handling module, electronic commerce data handling module, commercial product recommending module is all connected with information extraction, fusion, comparison module.
As shown in Figure 2, social media data handling module (comprises N reptile by web crawlers, reptile 1, reptile 2, reptile 3 ... reptile N) obtain the data on social media, obtain after data, process to many computing machines by the task allocation schedule that uses hadoop that many URL are captured, make the scheduling processing method of the load balancing of every computing machine give the distributed system that multi-section server forms, by HTML parser, webpage is analyzed, text analyzing, link analysis and web page quality control, duplicate removal, obtain corresponding web page contents, described web page contents result is divided into structured message (friend, the link informations such as group) and unstructured information (text, image etc.), store into respectively in structured message database and the Unstructured Information Database.
Structuring and non-structured classification can be by judging whether this content can store (as SQL) in structured database into and judge.Conventionally text and image are unstructured datas, content wherein cannot be carried out to cutting and classification.As one section of news, although know that there are the information such as name, place name, exabyte, time the inside, if do not processed, cannot import to these information in SQL automatically.Meanwhile, structured message and unstructured information also can repeat webpage analysis, text analyzing, link analysis and web page quality control, duplicate removal, the structured message that obtains simplifying and unstructured information.
As shown in Figure 3, electronic commerce data handling module produces a series of regular expression query statements by machine learning Query Builder for e-commerce website e-commerce website is inquired about, and all information scratchings are got off, carry out the data interaction of web page contents by DeepWeb query expansion, page analyzer.Learn the rule searching of different web sites by machine learning algorithm, use all inquiries with large probability of method traversal of keyword replacement, by knowledge base intelligent inquire, item property extracts, true value is found, generate entity attribute relational database,, also can be undertaken by historical data training classifier meanwhile.
For example, the inquiry of a known Taobao is " man, light, 40; running shoe, nike, interior look " to regular expression query statement, can be expanded by knowledge base, for example size, color, type, brand, can grab a greater variety of footwear, realizing crawl by analyzing related pages.
As shown in Figure 4, information extraction, fusion, comparison module comprise user modeling module, commodity MBM, commodity and user's mapping, modeling module, and user modeling module, commodity MBM are connected with user's mapping, modeling module with commodity.
User modeling module routine comprises:
1-1) label is propagated: for social networks user, carry out random walk by the figure of the label on social networks (label on social networks can be the label of user oneself mark) composition and obtain the probability that label is propagated, the mode of random walk is the good friend's network random walk consisting of user, thus the label of extending user;
1-2) content is differentiated: the content that user is delivered is analyzed, and uses topic model, entity to extract and obtains possible label; Meanwhile, by training machine learning classification device, the user of existing label is learnt, thereby the user who there is no label is carried out to label judgement; Topic model refers to the method for a class machine learning, the present embodiment uses Latent Dirichlet Allocation, in actual mechanical process, can be not limited to and use these class methods, even can represent a topic with high frequency keyword with text cluster or directly.
1-3) other information of user are differentiated: the content of delivering for user is understood and its circle of friends is analyzed, and then estimates user's age, job specification, work place, income information, thereby can better understand user's demand; When age, job specification, work place and income information to user is estimated, user is extracted to keyword and good friend's attributive character, use machine learning method, existing markup information is learnt to obtain sorter, unknown sample is classified.
Be first each user to be estimated to an age to the analysis of circle of friends, this age can be that he fills in the age on social networks, can be also that the content that we deliver him by model of initialization returns the obtained age.And then on social networks, be similar to the process that label is propagated, and this user's circle of friends is analyzed, obtain friend's age bracket statistics, from this user's of row amendment age estimation.
Commodity MBM operational process comprises,
2-1) property value is filled, capturing by webpage the item property obtaining may be comprehensive not, need to search for based on internet search engine, from corresponding summary and ad content, obtain possible property value, and the probability of statistics appearance, in internet, search, mate, add up the frequency of occurrences and carry out true value discovery.
2-2) commodity tree classification, obtains taxonomy-tree information at crawl commodity simultaneously and in addition the commodity that there is no classification tree information is classified, and is categorized on some nodes of commodity tree;
2-3) other informations of commodity, collect other information of commodity, and store in database, analyze by the structure to e-commerce website, are commented on accordingly and give a mark.
Mapping correspondence set up by commodity and user by commodity and user's mapping, modeling module, the content delivered when user is mentioned to dependent merchandise on social media=and e-commerce website on picture, the comment denoising of user to commodity and businessman, set up mapping model, mapping model is the direct feature extraction to data or the feature representation that obtains by the means of machine learning, obtain after mapping model, relatively commodity and user's correlativity.
Commercial product recommending module comprises recommended by client module and commodity end recommending module.
Recommended by client module operational process comprises the following steps:
3-1) obtain by the correlativity of user and commodity the commodity list that may recommend;
3-2) user's good friend is carried out to the analysis of the correlativity of user and commodity, and by good friend's commodity list, this user is voted;
3-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and described user's portrait comprises age, income and interest;
3-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of described social media comprises adds good friend, quotes good friend, personal letter, comment etc.;
Commercial product recommending module operational process comprises the following steps:
4-1) obtaining by commodity and user's correlativity may be to the interested user of these commodity;
4-2) user's good friend is carried out to commodity and user's correlation analysis, and by good friend's commodity list, this user is voted;
4-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and described user's portrait comprises age, income and interest;
4-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of described social media comprises adds good friend, quotes good friend, personal letter, comment etc.
Below be only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. the commercial product recommending system based on social media, is characterized in that, comprises social media data handling module, electronic commerce data handling module, information extraction, fusion, comparison module and commercial product recommending module;
Social media data handling module is carried out data crawl for social media, carries out multimachine capture data by parallel algorithms;
Electronic commerce data handling module is carried out data crawl for e-commerce website, carries out multimachine capture data by parallel algorithms, carries out comprehensive data interaction by DeepWeb query expansion;
Information extraction, fusion, comparison module, for to capturing the data analysis, the processing that obtain, carry out foundation and the mapping of commodity tree to commodity, social media user is carried out to modeling analysis;
Commercial product recommending module, for user to be recommended is carried out to merchandise query, sorts to the commodity that obtain, and recommends according to the degree of correlation, interest, circle of friends information;
Social media data handling module, electronic commerce data handling module, commercial product recommending module is all connected with information extraction, fusion, comparison module.
2. the commercial product recommending system based on social media according to claim 1, it is characterized in that, described social media data handling module is obtained the data on social media by web crawlers, obtain after data, distribute to many computing machines by task of using hadoop that many URL are captured, make the scheduling processing method of the load balancing of every computing machine give the distributed system that multi-section server forms, by HTML parser, webpage is analyzed, text analyzing, link analysis and web page quality control, duplicate removal, obtain corresponding web page contents, described web page contents result is divided into structured message and unstructured information, store into respectively in structured message database and the Unstructured Information Database.
3. the commercial product recommending system based on social media according to claim 1, it is characterized in that, described electronic commerce data handling module produces a series of regular expression query statements by machine learning Query Builder for e-commerce website e-commerce website is inquired about, and all information scratchings are got off, carry out the data interaction of web page contents by DeepWeb query expansion, page analyzer; Learn the rule searching of different web sites by machine learning algorithm, use all inquiries with large probability of method traversal of keyword replacement, by knowledge base intelligent inquire, item property extracts, and true value is found, generates entity attribute relational database.
4. the commercial product recommending system based on social media according to claim 1, it is characterized in that, described information extraction, fusion, comparison module comprise user modeling module, commodity MBM, commodity and user's mapping, modeling module, and user modeling module, commodity MBM are connected with user's mapping, modeling module with commodity.
5. the commercial product recommending system based on social media according to claim 4, is characterized in that, described user modeling module routine comprises:
1-1) label is propagated: for social networks user, carry out random walk by the figure of the label composition on social networks and obtain the probability that label is propagated, thus the label of extending user;
1-2) content is differentiated: the content that user is delivered is analyzed, and uses topic model, entity to extract and obtains possible label; Meanwhile, by training machine learning classification device, the user of existing label is learnt, thereby the user who there is no label is carried out to label judgement;
1-3) other information of user are differentiated: the content of delivering for user is understood and its circle of friends is analyzed, and then estimates user's age, job specification, work place, income information, thereby can better understand user's demand; When age, job specification, work place and income information to user is estimated, user is extracted to keyword and good friend's attributive character, use machine learning method, existing markup information is learnt to obtain sorter, unknown sample is classified.
6. the commercial product recommending system based on social media according to claim 4, is characterized in that, described commodity MBM operational process comprises,
2-1) property value is filled, capturing by webpage the item property obtaining may be comprehensive not, need to search for based on internet search engine, from corresponding summary and ad content, obtain possible property value, and the probability of statistics appearance, in internet, search, mate, add up the frequency of occurrences and carry out true value discovery;
2-2) commodity tree classification, obtains taxonomy-tree information at crawl commodity simultaneously and in addition the commodity that there is no classification tree information is classified, and is categorized on some nodes of commodity tree;
2-3) other information acquisitions of commodity, collect other information of commodity, and store in database, analyze by the structure to e-commerce website, are commented on accordingly and give a mark.
7. the commercial product recommending system based on social media according to claim 4, it is characterized in that, mapping correspondence set up by commodity and user by described commodity and user's mapping, modeling module, picture on content and the e-commerce website of delivering when user is mentioned to dependent merchandise on social media, the comment denoising of user to commodity and businessman, set up mapping model, mapping model is the direct feature extraction to data or the feature representation that obtains by the means of machine learning, obtain after mapping model, relatively commodity and user's correlativity.
8. the commercial product recommending system based on social media according to claim 1, is characterized in that, described commercial product recommending module comprises recommended by client module and commodity end recommending module.
9. the commercial product recommending system based on social media according to claim 8, is characterized in that, described recommended by client module operational process comprises the following steps:
3-1) obtain by the correlativity of user and commodity the commodity list that may recommend;
3-2) user's good friend is carried out to the analysis of the correlativity of user and commodity, and by good friend's commodity list, this user is voted;
3-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and described user's portrait comprises age, income and interest;
3-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of described social media comprises adds good friend, quotes good friend, personal letter, comment etc.
10. the commercial product recommending system based on social media according to claim 8, is characterized in that, described commercial product recommending module operational process comprises the following steps:
4-1) obtaining by commodity and user's correlativity may be to the interested user of these commodity;
4-2) user's good friend is carried out to commodity and user's correlation analysis, and by good friend's commodity list, this user is voted;
4-3) drawn a portrait commercial product recommending is further processed by analysis user, target is recommended in segmentation, and described user's portrait comprises age, income and interest;
4-4) interactive mode by social media is carried out commercial product recommending for this user, and the interactive mode of described social media comprises adds good friend, quotes good friend, personal letter, comment.
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Cited By (72)
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